A Convolutional Neural Network-Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array

Mingxuan Mao

doi:10.1109/TIM.2022.3227552

A Convolutional Neural Network-Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array

2023-01-01

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DOI：10.1109/TIM.2022.3227552

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The shadows formed by fast-moving vehicles on a pavement PV array exhibit complex dynamic random distribution characteristics, which can cause a dynamic multipeak PV curve. Dynamic vehicle shadow will cause a reduction in pavement PV power, so the question is how to maximize the power in such conditions by operating at different maximum power point (MPP) quickly and continually. To address this issue, this article proposes an MPP voltage forecasting method based on convolutional neural network (CNN). This method inputs the environmental information of pavement PV array into the proposed CNN model for learning and then uses this model to forecast the MPP voltage. Finally, simulation and experimental test with ResNet, MLP, and CNN methods are carried out and the comparison results show that this model can accurately predict the MPP voltage of pavement PV array under different vehicle shading conditions.

Keywords:

Prediction algorithms

Convolutional neural networks

Machine learning algorithms

Forecasting

Roads

Neural networks

Classification algorithms

Convolutional neural network (CNN)

feature extraction

maximum power point (MPP) voltage forecasting model

pavement PV array

vehicle shadow image

A Convolutional Neural Network-Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array

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